BackgroundThis study is an assessment of the impact of acquisition times on SUV with [18F]FDG-PET/CT on healthy livers (reference organ with stable uptake over time) and on tumors.MethodsOne hundred six [18F]FDG-PET/CT were acquired in list mode over a single-bed position (livers (n = 48) or on tumors (n = 58)). Six independent datasets of different durations were reconstructed (from 1.5 to 10 min). SUVmax (hottest voxel), SUVpeak (maximum average SUV within a 1-cm3 spherical volume), and SUVaverage were measured within a 3-cm-diameter volume of interest (VOI) in the right lobe of the liver. For [18F]FDG avid tumors (SUVmax ≥ 5), the SUVmax, SUVpeak, and SUV41% (isocontour threshold method) were computed.ResultsFor tumors, SUVpeak values did not vary with acquisition time. SUVmax displayed significant differences between 1.5- and 5–10-min reconstruction times. SUV41% was the most time-dependent parameter. For the liver, the SUVaverage was the sole parameter that did not vary over time.ConclusionsFor [18F]FDG avid tumors, with short acquisition times, i.e., with new generations of PET systems, the SUVpeak may be more robust than the SUVmax. The SUVaverage over a 3-cm-diameter VOI in the right lobe of the liver appears to be a good method for a robust and reproducible assessment of the hepatic metabolism.

Purpose To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm. Material and methods Acquisitions on the CT ACR 464 phantom were performed at five dose levels (CTDIvol: 10/7.5/5/2.5/1 mGy) using chest or abdomen pelvis protocol parameters. Raw data were reconstructed using the filtered‐back projection (FBP), the enhanced level of AIDR 3D (AIDR 3De), and the three levels of AiCE (Mild, Standard, and Strong) for the two versions (AiCE V8 vs AiCE V10). The noise power spectrum (NPS) and task‐based transfer function (TTF) for bone (high‐contrast insert) and acrylic (low‐contrast insert) inserts were computed. To quantify the changes of noise magnitude and texture, the square root of the area under the NPS curve and the average spatial frequency (fav) of the NPS curve were measured. The detectability index (d’) was computed to model the detectability of either a large mass in the liver or lung, or a small calcification or high contrast tissue boundaries. Results The noise magnitude was lower with both AiCE versions than with AIDR 3De. The noise magnitude was lower with AiCE V10 than with AiCE V8 (‐4 ± 6% for Mild, ‐13 ± 3% for Standard, and ‐48 ± 0% for Strong levels). fav and TTF50% values for both inserts shifted towards higher frequencies with AiCE than with AIDR 3De. Compared to AiCE V08, fav shifted towards higher frequencies with AiCE V10 (45 ± 4%, 36 ± 3%, and 5 ± 4% for all levels, respectively). The TTF50% values shifted towards higher frequencies with AiCE V10 as compared with AiCE V8 for both inserts, except for the Strong level for the acrylic insert. Whatever the dose and AiCE levels, d’ values were higher with AiCE V10 than with AiCE V8 for the small object/calcification and for the large object/lesion. Conclusion As compared to AIDR 3De, lower noise magnitude and higher spatial resolution and detectability index were found with both versions of AiCE. As compared to AiCE V8, AiCE V10 reduced noise and improved spatial resolution and detectability without changing the noise texture in a simple geometric phantom, except for the Strong level. AiCE V10 seems to have a greater potential for dose reduction than AiCE V8.

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Ion-induced cell killing has been reported to depend on the irradiation dose but also on the projectile parameters. In this paper we focus on two approaches developed and extensively used to predict cell survival in response to ion irradiation: the Local Effect Model and the Katz Model.These models are based on a track-structure description summarized in the concept of radial dose. This latter is sensitive to ion characteristics